Neuro Fuzzy Inference Approach : A Survey

Fuzzy Logic is an extension of classical logic which provides an effective mathematical tool to represent information in a way that resembles natural human reasoning and deals with system uncertainty and vagueness. ANN is a biologically inspired computational structure comprised of densely interconnected adaptive simple processing elements that are capable of performing massively parallel computations for data processing and knowledge representation. The combination of fuzzy inference system and artificial neural network have attracted the researchers and scholars in various scientific and engineering areas to the growing need of adaptive intelligent systems. Artificial neural network are not good at explaining how they reach their decisions whereas fuzzy systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. Due to these limitations an intelligent hybrid systems where two or more techniques are combined in a manner that overcomes the problems of individual techniques are created. Any type of systems that combine these two techniques can be called Neuro-Fuzzy systems. Neuro-Fuzzy systems are systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. This type of system is characterized by a fuzzy system where fuzzy sets and fuzzy rules are adjusted using input output patterns. There are several types of neuro-fuzzy systems where each author defined its own model. This survey paper describes the most known hybrid neuro-fuzzy techniques, with their advantages and Limitations.